ARTIFICIAL INTELLIGENCE IN TELECOMMUNICATION MARKET ANALYSIS

Artificial Intelligence In Telecommunication MarketSize and Trends

Market Trends

Artificial intelligence (AI) is group of methodology that focus on formation of intelligent machines with the help of human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. The main application of artificial intelligence in telecommunications is for network management. The two key technologies that are widely in telecommunication industry are expert systems and machine learning. However, AI is expected to be more beneficial in telecom industry, if the operators upgrade their networks to Software Defined Networks (SDNs), which leads to network virtualization and the deployment of relatively better cloud-based services.

Advent of The fifth generation of mobile networks (5G) and Internet of Things (IoT) technologies, to build future networks is expected to aid in integration of AI in telecom industry. Mobile networks have to deal with heterogeneous data coming from all over the world and from a huge variety of systems, retailers and network types and they should have the ability to act in real-time. So, the analysis of these huge data sets from all over the world is time consuming and somewhere it is next to impossible. In this case Artificial Intelligence plays a key role because it is used to predict and analyze issues faster than human. Artificial Intelligence will make the fifth generation of mobile networks more open enabling connectivity to predictability.

To solve these issues in the telecom industry, machine learning tools are used, which is used to tackle the churning prediction problem. The methods used for solving churning problem includes artificial neural networks, decision trees learning, regression analysis, logistic regression, support vector machines, naive Bayes, sequential pattern mining and market basket analysis, linear discriminant analysis, and rough set approach. Hence, the use of machine learning helps to detect fraudulent calls in mobile phones by examining the user’s calling behavior.